A Non-Asymptotic Analysis of Non-Parametric Temporal-Difference Learning
Abstract
Temporal-difference learning is a popular algorithm for policy evaluation. In this paper, we study the convergence of the regularized non-parametric TD(0) algorithm, in both the independent and Markovian observation settings. In particular, when TD is performed in a universal reproducing kernel Hilbert space (RKHS), we prove convergence of the averaged iterates to the optimal value function, even when it does not belong to the RKHS. We provide explicit convergence rates that depend on a source condition relating the regularity of the optimal value function to the RKHS. We illustrate this convergence numerically on a simple continuous-state Markov reward process.
Cite
Text
Berthier et al. "A Non-Asymptotic Analysis of Non-Parametric Temporal-Difference Learning." Neural Information Processing Systems, 2022.Markdown
[Berthier et al. "A Non-Asymptotic Analysis of Non-Parametric Temporal-Difference Learning." Neural Information Processing Systems, 2022.](https://mlanthology.org/neurips/2022/berthier2022neurips-nonasymptotic/)BibTeX
@inproceedings{berthier2022neurips-nonasymptotic,
title = {{A Non-Asymptotic Analysis of Non-Parametric Temporal-Difference Learning}},
author = {Berthier, Eloïse and Kobeissi, Ziad and Bach, Francis R.},
booktitle = {Neural Information Processing Systems},
year = {2022},
url = {https://mlanthology.org/neurips/2022/berthier2022neurips-nonasymptotic/}
}